利用中国区域地面气象要素数据集制作的大气强迫场驱动通用陆面模式CLM4.5(Community Land M odel version 4.5)对青藏高原区域进行离线模拟试验,模拟结果与D66、沱沱河(TTH)和玛曲(Maqu)3个站点的观测资料以及GLDAS(Global Land Data A...利用中国区域地面气象要素数据集制作的大气强迫场驱动通用陆面模式CLM4.5(Community Land M odel version 4.5)对青藏高原区域进行离线模拟试验,模拟结果与D66、沱沱河(TTH)和玛曲(Maqu)3个站点的观测资料以及GLDAS(Global Land Data Assimilation System)-CLM2模拟结果进行了对比,并分析了陆面模式对冻融过程中土壤温度和湿度模拟的偏差及其可能原因。结果表明:CLM4.5对土壤温度模拟较好(平均RM SE≈3℃),而GLDAS-CLM2计算的土壤温度偏高,偏差较大(平均RMSE>6℃),且其偏差大于CLM4.5,尤其在冻融期;CLM4.5能较好地模拟出冻融过程中土壤湿度季节变化,但土壤湿度的模拟值与观测值存在一定偏差(平均RMSE≈0.1 mm3·mm-3),GLDAS-CLM2不能反映出土壤湿度在冻融过程中的变化特征。CLM4.5的模拟偏差主要来自大气强迫场,而GLDAS-CLM2的偏差除了大气强迫场的不确定性外,还来自于模式冻融参数化方案的不完善。大气强迫场中的气温和降水对土壤温度和湿度的影响在冻融期和非冻融期表现不同。在非冻融期,土壤温度的模拟主要受气温的影响(r>0.6),气温偏差对土壤温度偏差的贡献率大于50%;土壤湿度的变化则主要受降水的影响,降水偏差对土壤湿度偏差的贡献率为20%~40%。在冻融期,受土壤水热相互作用的影响,气温和降水对土壤温度和湿度的作用效果减弱;土壤湿度的变化受气温影响显著,其贡献率为10%~20%。陆面模式中冻融参数方案的不完善是冻融过程中土壤温度和湿度偏差的重要来源之一。展开更多
This paper focus on the Modeling and Calculation of DC current distribution in AC power grid induced under HVDC Ground-Return-Mode. Applying complex image method and boundary element method, a new field-circuit coupli...This paper focus on the Modeling and Calculation of DC current distribution in AC power grid induced under HVDC Ground-Return-Mode. Applying complex image method and boundary element method, a new field-circuit coupling model was set up. Based on the calculation result with complex image method, this paper derived the modification factor for induced earth potential from practical measurement, which increased the accuracy of calculation. The modification method is helpful for evaluation on the effect of means used for blocking the dc-bias current in transformer neutral and also useful for the forecast of the DC current distribution when the power grid is in different line connection mode. The DC distribution character in Guangdong power grid is shown and suggestion is proposed that the mitigation of dc-bias should start from those substations whose earth-potential is highest.展开更多
从平面波作用下三维声强阵列的理论推导出发,通过声压、质点振速估计和声强谱计算,比较了正四面体传声器和六传声器的幅值误差和方向性误差,并在全消声室中对数值仿真的结果进行验证.研究结果表明:随着频率增加,幅值误差呈指数式增长,4 ...从平面波作用下三维声强阵列的理论推导出发,通过声压、质点振速估计和声强谱计算,比较了正四面体传声器和六传声器的幅值误差和方向性误差,并在全消声室中对数值仿真的结果进行验证.研究结果表明:随着频率增加,幅值误差呈指数式增长,4 k Hz范围内两种传声器的幅值误差均小于0.5 d B,满足精度要求,且正四面体传声器略优于六传声器;在平面波作用下正四面体传声器的方向性精度明显优于六传声器,当频率为8 k Hz时六传声器的角度偏差已接近6°,而正四面体传声器角度偏差仅为1°,且全频域范围内角度偏差增长缓慢.展开更多
Purpose–With the aid of naturalistic simulations,this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.Design/methodology/approach–The simulation environment i...Purpose–With the aid of naturalistic simulations,this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.Design/methodology/approach–The simulation environment is established by integrating virtual reality interface with a micro-simulation model.In the simulation,the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms.Humansubject experiments are carried,and participants are asked to virtually sit in the developed autonomous vehicle(AV)that allows for both human driving and autopilot functions within a mixed traffic environment.Findings–Not surprisingly,the inconsistency is identified between two driving modes,in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe.Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage,participants still frequently intervened during the AV operation.On a similar note,even though the statistical results reflect that the AV drives under perceived high-risk conditions,rarely an actual crash can happen.This suggests that the classic safety surrogate measurement,e.g.time-tocollision,may require adjustment for the mixed traffic flow.Research limitations/implications–Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important,where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.Practical implications–This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.Social implications–This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios(i.e.multiple scenes and various traffic conditions)to facilitate user acceptance of AV technology.Originality/v展开更多
Rainfall over Rwanda is highly variable both in space and time. This variability leads to chronic food insecurity due to the overdependence of the economy on rain-fed agriculture systems. This study aims to evaluate t...Rainfall over Rwanda is highly variable both in space and time. This variability leads to chronic food insecurity due to the overdependence of the economy on rain-fed agriculture systems. This study aims to evaluate the skills of Rossby Centre Regional Climate Model (RCA4)</span><b> </b><span style="font-family:Verdana;">simulations driven by 10 GCMs for the period 1951-2005 using the Global Precipitation Climatology Centre (GPCC v8) as a reference. Different statistical and geospatial metrics were used to deduce the model’s skills in simulating seasonal and annual rainfall. Results show that the country received bimodal rainfall pattern;March-May (MAM) and September-December (SOND). The RCA4 models are inconsistent in simulating the MAM rainy peak. However, the models are coherent in simulating SOND seasonal peak despite exhibiting wet bias. The models show reasonable skills in simulating mean annual cycle than interannual variability as depicted by insignificant correlation and different signs of rainfall trend. Conclusively, the performance of RCA4 models in simulating observed rainfall characteristics over Rwanda is relatively weak. The performance of the models differs at various time scales. Nevertheless, the models can be ranked from the best performing to the least as;CSIRO, CanESM2, CNRM, GFDL, MIROC5, ENS, EC-Earth, HadGEM2, IPSL, MPI, and NorESM1. Ranking the performance of RCA4 historical models acts as a basis for future climate model’s selection depending on the purpose of the study. The findings of this study may help in devising appropriate climate adaptation measures to respond to the ongoing global warming for sustainable economic and livelihood development. Additionally, modelers may improve the model’s parametrization schemes and lessen the inherent chronic biases for a better presentation of the future.展开更多
文摘利用中国区域地面气象要素数据集制作的大气强迫场驱动通用陆面模式CLM4.5(Community Land M odel version 4.5)对青藏高原区域进行离线模拟试验,模拟结果与D66、沱沱河(TTH)和玛曲(Maqu)3个站点的观测资料以及GLDAS(Global Land Data Assimilation System)-CLM2模拟结果进行了对比,并分析了陆面模式对冻融过程中土壤温度和湿度模拟的偏差及其可能原因。结果表明:CLM4.5对土壤温度模拟较好(平均RM SE≈3℃),而GLDAS-CLM2计算的土壤温度偏高,偏差较大(平均RMSE>6℃),且其偏差大于CLM4.5,尤其在冻融期;CLM4.5能较好地模拟出冻融过程中土壤湿度季节变化,但土壤湿度的模拟值与观测值存在一定偏差(平均RMSE≈0.1 mm3·mm-3),GLDAS-CLM2不能反映出土壤湿度在冻融过程中的变化特征。CLM4.5的模拟偏差主要来自大气强迫场,而GLDAS-CLM2的偏差除了大气强迫场的不确定性外,还来自于模式冻融参数化方案的不完善。大气强迫场中的气温和降水对土壤温度和湿度的影响在冻融期和非冻融期表现不同。在非冻融期,土壤温度的模拟主要受气温的影响(r>0.6),气温偏差对土壤温度偏差的贡献率大于50%;土壤湿度的变化则主要受降水的影响,降水偏差对土壤湿度偏差的贡献率为20%~40%。在冻融期,受土壤水热相互作用的影响,气温和降水对土壤温度和湿度的作用效果减弱;土壤湿度的变化受气温影响显著,其贡献率为10%~20%。陆面模式中冻融参数方案的不完善是冻融过程中土壤温度和湿度偏差的重要来源之一。
文摘This paper focus on the Modeling and Calculation of DC current distribution in AC power grid induced under HVDC Ground-Return-Mode. Applying complex image method and boundary element method, a new field-circuit coupling model was set up. Based on the calculation result with complex image method, this paper derived the modification factor for induced earth potential from practical measurement, which increased the accuracy of calculation. The modification method is helpful for evaluation on the effect of means used for blocking the dc-bias current in transformer neutral and also useful for the forecast of the DC current distribution when the power grid is in different line connection mode. The DC distribution character in Guangdong power grid is shown and suggestion is proposed that the mitigation of dc-bias should start from those substations whose earth-potential is highest.
文摘从平面波作用下三维声强阵列的理论推导出发,通过声压、质点振速估计和声强谱计算,比较了正四面体传声器和六传声器的幅值误差和方向性误差,并在全消声室中对数值仿真的结果进行验证.研究结果表明:随着频率增加,幅值误差呈指数式增长,4 k Hz范围内两种传声器的幅值误差均小于0.5 d B,满足精度要求,且正四面体传声器略优于六传声器;在平面波作用下正四面体传声器的方向性精度明显优于六传声器,当频率为8 k Hz时六传声器的角度偏差已接近6°,而正四面体传声器角度偏差仅为1°,且全频域范围内角度偏差增长缓慢.
文摘Purpose–With the aid of naturalistic simulations,this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.Design/methodology/approach–The simulation environment is established by integrating virtual reality interface with a micro-simulation model.In the simulation,the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms.Humansubject experiments are carried,and participants are asked to virtually sit in the developed autonomous vehicle(AV)that allows for both human driving and autopilot functions within a mixed traffic environment.Findings–Not surprisingly,the inconsistency is identified between two driving modes,in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe.Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage,participants still frequently intervened during the AV operation.On a similar note,even though the statistical results reflect that the AV drives under perceived high-risk conditions,rarely an actual crash can happen.This suggests that the classic safety surrogate measurement,e.g.time-tocollision,may require adjustment for the mixed traffic flow.Research limitations/implications–Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important,where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.Practical implications–This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.Social implications–This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios(i.e.multiple scenes and various traffic conditions)to facilitate user acceptance of AV technology.Originality/v
文摘Rainfall over Rwanda is highly variable both in space and time. This variability leads to chronic food insecurity due to the overdependence of the economy on rain-fed agriculture systems. This study aims to evaluate the skills of Rossby Centre Regional Climate Model (RCA4)</span><b> </b><span style="font-family:Verdana;">simulations driven by 10 GCMs for the period 1951-2005 using the Global Precipitation Climatology Centre (GPCC v8) as a reference. Different statistical and geospatial metrics were used to deduce the model’s skills in simulating seasonal and annual rainfall. Results show that the country received bimodal rainfall pattern;March-May (MAM) and September-December (SOND). The RCA4 models are inconsistent in simulating the MAM rainy peak. However, the models are coherent in simulating SOND seasonal peak despite exhibiting wet bias. The models show reasonable skills in simulating mean annual cycle than interannual variability as depicted by insignificant correlation and different signs of rainfall trend. Conclusively, the performance of RCA4 models in simulating observed rainfall characteristics over Rwanda is relatively weak. The performance of the models differs at various time scales. Nevertheless, the models can be ranked from the best performing to the least as;CSIRO, CanESM2, CNRM, GFDL, MIROC5, ENS, EC-Earth, HadGEM2, IPSL, MPI, and NorESM1. Ranking the performance of RCA4 historical models acts as a basis for future climate model’s selection depending on the purpose of the study. The findings of this study may help in devising appropriate climate adaptation measures to respond to the ongoing global warming for sustainable economic and livelihood development. Additionally, modelers may improve the model’s parametrization schemes and lessen the inherent chronic biases for a better presentation of the future.
